Data Scientist SME

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Must not include Job ID etc. Must preserve all substantive info. Remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions. We need to parse the description. Key content: - Description paragraph: "Turn complex data into mission advantage..." etc. - Position may be performed in locations: Gaithersburg, MD primary; Alexandria, VA; Chantilly, VA; Aurora, CO; St. Louis, MO. - Clearance Level Required: Active Top Secret/SCI clearance with ability to pass Polygraph. - Primary Responsibilities: list bullet points. - Basic Qualifications: list. - Preferred Qualifications: list. - There's also "#Chinook #NSBA" etc. - Then some marketing fluff: "If you're looking for comfort, keep scrolling..." etc. That's likely boilerplate? It's not standard EEO but maybe considered fluff; we should remove? The instruction: remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The marketing fluff might be considered not boilerplate but maybe not substantive? It says "If you're looking for comfort, keep scrolling..." That's likely filler; we should remove as it's not substantive job content. Also "Step 10 is ancient history..." etc. Probably remove. - Original Posting date etc: maybe not needed? It's metadata; could be folded into short p or dropped. Probably drop. - Pay Range: $131,300.00 - $237,350.00. Also note about pay range being guideline. - Pay And Benefits section: mentions competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details at link. - Benefits: we have that. - Schedule: not mentioned explicitly. No schedule info. So we should not create Schedule section. - About Leidos: maybe could be About the role? Actually "About Leidos" is company overview; could be placed under About the role? The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we should have About the role (maybe includes description and mission). Could also include About Leidos? Might be considered About the role? Probably we can put the initial description under About the role. - Requirements: maybe Basic Qualifications. - Qualifications: maybe Preferred Qualifications. - Skills: maybe list of technical skills from Basic Qualifications (Python, etc). Could combine. We need to avoid duplication. Let's extract substantive content: About the role: The description paragraph(s) about turning data into mission advantage, role as Data Scientist SME, working with team, etc. Also location info and clearance maybe could be in About the role or separate? Clearance is a requirement; could go under Requirements. Responsibilities: the bullet list under Primary Responsibilities. Requirements: Basic Qualifications (US citizenship, degree, years experience, etc). Also clearance. Qualifications: Preferred Qualifications. Skills: maybe list of specific technical skills: Python, Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, PyTorch, Spark, PySpark, Kafka, Hadoop, AWS/Azure, GIS, ArcGIS, GeoPandas, Rasterio, GDAL, GeoTIFF, Shapefiles, WMS, WFS, Tableau, PowerBI, etc. Could be a subsection. Benefits: from Pay And Benefits: competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. Also maybe mention more details link. Pay: Pay Range $131,300.00 - $237,350.00. Schedule: none -> omit. We must not include boilerplate like "If you're looking for comfort..." etc. Also remove duplicated lines: there may be duplicate of "Pay Range" etc. We must preserve all substantive info; not invent. Now produce HTML fragment with

headings and appropriate

or

• . We need to decide structure:

  About the role

  Turn complex data into mission advantage. Join Leidos on the Chinook Program supporting our Intelligence Community customer and play a critical role in transforming massive, complex datasets into actionable intelligence and mission insights. As a Data Scientist SME, you will bring advanced analytical expertise, innovative thinking, and technical leadership to a high performing Agile engineering team delivering next generation capabilities to our Intelligence Community customer. This is an opportunity to work at the intersection of data science, artificial intelligence, machine learning, cloud technology, and national security, where your expertise can directly influence how mission users understand data, identify patterns, and make critical decisions. You will serve as a technical authority while collaborating with software engineers, DevSecOps professionals, system architects, analysts, and mission stakeholders to solve challenging problems that don’t have off-the-shelf answers. If you are energized by applying advanced analytics to real-world mission challenges and want to see your work have a tangible impact, this is an opportunity to make a difference. Leidos National Security Sector combines technology-enabled services and mission software capabilities in the areas of cyber, logistics, security operations, and decision analytics to support our defense and intel customers’ mission to defend against evolving threats around the world. Our team’s focus is to ensure our customers have the right tools, technologies, and tactics to keep pace with an ever-evolving security landscape and succeed in their pursuit to protect people and critical assets.

  Maybe also include location and clearance? Could be separate but we can include in About the role? Might be better to put location and clearance under Requirements? Actually clearance is a requirement; location is info about where job may be performed. Could be in About the role as additional detail. But we have separate sections for Requirements and Qualifications; location could be in About the role as a note. Let's add a sentence: "Position may be performed in Gaithersburg, MD (primary), Alexandria, VA, Chantilly, VA, Aurora, CO, or St. Louis, MO. An active Top Secret/SCI clearance with the ability to pass a Polygraph examination is required." That belongs maybe under Requirements? But we can put in About the role as it's part of role description. However the instruction: "Use

  for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." So we can include location and clearance under Requirements (since it's a requirement). Let's do that. Thus About the role: just the descriptive narrative (excluding location/clearance). We'll keep location/clearance in Requirements. Now Responsibilities: list bullet points from Primary Responsibilities. We need to capture each bullet: - Serve as the technical SME in data science and geospatial analytics, providing high-level guidance on complex technical problems related to the GEOINT mission. - Lead the design and development of sophisticated data science models and machine learning algorithms to analyze large, multi-source geospatial data sets (e.g., satellite imagery, sensor data, geospatial databases). - Apply deep knowledge of GEOINT, remote sensing, and spatial analysis to develop and implement solutions that enhance data-driven decision-making capabilities. - Provide strategic recommendations for leveraging new and emerging technologies, including machine learning, AI, and cloud platforms, to improve analytical workflows, efficiency, and mission outcomes. - Mentor and train junior data scientists and analysts, ensuring the application of best practices in data science and the development of mission-relevant expertise. - Work closely with GEOINT analysts, program managers, engineers, and other stakeholders to identify critical requirements, define technical approaches, and ensure that solutions align with mission objectives. - Conduct applied research to explore innovative data science techniques and emerging trends (e.g., deep learning, reinforcement learning, advanced geospatial algorithms) that can be applied to solve real-world intelligence problems. - Oversee the deployment and operationalization of data science models, ensuring they are scalable, reliable, and deliver actionable insights to GEOINT decision-makers. - Maintain high-quality documentation for models, methodologies, and analysis processes to support reproducibility, training, and knowledge transfer. Now Requirements (Basic Qualifications + clearance + location + US citizenship). Let's list: - US citizenship is required per contract. - Bachelor’s degree in Data Science, Computer Science, Geospatial Science or related field and 12-15 years of prior relevant experience or Master’s with 10-13 years of prior relevant experience. May possess a Doctorate in technical domain. - 10+ years of professional experience in data science. - 5+ years of experience with GEOINT or geospatial data analysis. - Proven expertise in developing and implementing machine learning and AI models, particularly in context of geospatial or remote sensing. - Deep knowledge of geospatial analytics tools (e.g., GIS, ArcGIS), remote sensing techniques, and the application of data science in the IC. - Expert proficiency in Python (or similar languages) and experience with data science libraries (Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, PyTorch, or equivalent technologies). - Strong experience with big data processing tools (e.g., Spark, PySpark, Kafka, Hadoop, AWS or Azure cloud platforms). - Expertise in working with geospatial data formats (e.g., GeoTIFF, Shapefiles, WMS, WFS) and spatial libraries (e.g., GeoPandas, Rasterio, GDAL). - Advance experience in developing and operationalizing AI/ML models and algorithms for geospatial data (e.g., object detection from satellite imagery, spatial clustering, predictive analytics). - Strong background in data visualization and reporting tools (e.g., Tableau, PowerBI). - Strong leadership, communication, and collaboration skills, with the ability to work directly with senior government officials, analysts, and technical teams. - Excellent problem-solving and analytical skills with a demonstrated ability to translate technical challenges into actionable insights. - Active Top Secret/SCI clearance with the ability to successfully pass a Polygraph examination. - Position may be performed in Gaithersburg, MD (primary), Alexandria, VA, Chantilly, VA, Aurora, CO, or St. Louis, MO. Now Qualifications (Preferred Qualifications): - Advanced certifications in data science or machine learning. - Familiarity with the customer’s mission and specific geospatial intelligence challenges. - Expertise in advanced deep learning techniques, particularly in computer vision and image analysis for geospatial applications. - Experience with cloud-native applications and distributed computing in a geospatial context. - Familiarity with satellite data analysis, and remoting sensing models. - Published research or technical papers in geospatial intelligence, machine learning, or data science. Now Skills: maybe we can extract a concise list of technical skills from Basic Qualifications that are not already covered? But we might duplicate. The instruction: prefer a few substantive sections over many thin ones. Could combine Skills into Requirements? But they asked to use Skills heading if there is real specific content. We have many skills listed; we could create a Skills section summarizing key technical skills: Python, Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, PyTorch, Spark, PySpark, Kafka, Hadoop, AWS/Azure, GIS, ArcGIS, GeoPandas, Rasterio, GDAL, GeoTIFF, Shapefiles, WMS, WFS, Tableau, PowerBI. Also leadership etc. But leadership is more soft skill; could be in Requirements. We could decide to not create a separate Skills section and instead include those skills under Requirements (since they are qualifications). However the instruction says we can have Skills section; but we must not create thin sections. If we put skills under Requirements, it's fine. But we already have many bullet points; maybe we can keep Requirements as is and not have Skills. Benefits: from Pay And Benefits: competitive compensation, Health and Wellness programs,